#!/usr/bin/env python3

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://spdx.org/licenses/BSD-3-Clause.html
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



"""Train a classification model."""

import pycls.core.config as config
import pycls.core.distributed as dist
import pycls.core.trainer as trainer
from pycls.core.config import cfg
import argparse,sys,os,torch
import torch
if torch.__version__>= '1.8':
    import torch_npu

def init_process_group(proc_rank, world_size, device_type="npu", port="29588"):
    """Initializes the default process group."""

    # Initialize the process group
    print("==================================")    
    print('Begin init_process_group')
    os.environ['MASTER_ADDR'] = '127.0.0.1'
    os.environ['MASTER_PORT'] = port
    if device_type == "npu":
        torch.distributed.init_process_group(
            backend=cfg.DIST_BACKEND,
            world_size=world_size,
            rank=proc_rank
        )
    elif device_type == "gpu":
        torch.distributed.init_process_group(
            backend=cfg.DIST_BACKEND,
            init_method="tcp://{}:{}".format("127.0.0.1", port),
            world_size=world_size,
            rank=proc_rank
        )        

    print("==================================")
    print("Done init_process_group")

    # Set the GPU to use
    #torch.cuda.set_device(proc_rank)
    if device_type == "npu":
        torch.npu.set_device(proc_rank)
    elif device_type == "gpu":
        torch.cuda.set_device(proc_rank)
    print('Done set device', device_type, cfg.DIST_BACKEND, world_size, proc_rank)

def main():
    """Load config from command line arguments and set any specified options."""
    parser = argparse.ArgumentParser(description="Config file options.")
    parser.add_argument("--device", help="gpu or npu", default="npu", type=str)
    parser.add_argument("--profperf", help="0 or 1", default=0, type=int)
    help_s = "Config file location"
    parser.add_argument("--cfg", dest="cfg_file", help=help_s, required=True, type=str)
    parser.add_argument("--rank_id", dest="rank_id", default=0, type=int)
    parser.add_argument("--device_id", dest="device_id", default=0, type=int)
    help_s = "See pycls/core/config.py for all options"
    parser.add_argument("opts", help=help_s, default=None, nargs=argparse.REMAINDER)
    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)
    args = parser.parse_args()
    print(args)

    config.merge_from_file(args.cfg_file)
    config._C.merge_from_list(args.opts)
    config.assert_and_infer_cfg()
    cfg.freeze()

    if cfg.NUM_GPUS > 1:
        init_process_group(proc_rank=args.rank_id, world_size=cfg.NUM_GPUS, device_type=args.device)
    elif args.device == "npu":
        torch.npu.set_device(args.device_id)
    elif args.device == "gpu":
        torch.cuda.set_device(args.device_id)

    if args.device == "npu":  
        cur_device = torch.npu.current_device()
    elif args.device == "gpu":
        cur_device = torch.cuda.current_device()
    print('cur_device: ', cur_device)

    trainer.train_model(args.device, args.profperf)


if __name__ == "__main__":
    main()
